Stochastic Computation for AI: Bayesian Inference, Uncertainty, and Optimization
Murali Krishna Pasupuleti · 2025
Abstract: Stochastic computation is a fundamental approach in artificial intelligence (AI) that enables probabilistic reasoning, uncertainty quantification, and robust decision-making in complex environments. This research explores the theoretical foundations, computational techniques, and real-world applications of stochastic methods, focusing on Bayesian inference, Monte Carlo methods, stochastic optimization, and uncertainty-aware AI models. Key topics include probabilistic graphical models, Markov Chain Monte Carlo (MCMC), variational inference, stochastic gradient descent (SGD), and Bayesian deep learning. These techniques enhance AI's ability to handle uncertain, noisy, and high-dimensional data while ensuring scalability, interpretability, and trustworthiness in applications such as robotics, financial modeling, autonomous systems, and healthcare AI. Case studies demonstrate how stochastic computation improves self-driving car navigation, financial risk assessment, personalized medicine, and reinforcement learning-based automation. The findings underscore the importance of integrating probabilistic modeling with deep learning, reinforcement learning, and optimization techniques to develop AI systems that are more adaptable, scalable, and uncertainty-aware. Keywords Stochastic computation, Bayesian inference, probabilistic AI, Monte Carlo methods, Markov Chain Monte Carlo (MCMC), variational inference, uncertainty quantification, stochastic optimization, Bayesian deep learning, reinforcement learning, probabilistic graphical models, stochastic gradient descent (SGD), uncertainty-aware AI, probabilistic reasoning, risk assessment, AI in robotics, AI in finance, AI in healthcare, decision-making under uncertainty, trustworthiness in AI, scalable AI, interpretable AI.